Author name cluster

Simos Gerasimou

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4 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Safe But Not Sorry: Reducing Over-Conservatism in Safety Critics via Uncertainty-Aware Modulation

Daniel Bethell
Simos Gerasimou
Radu Calinescu
Calum Imrie

Ensuring safe exploration in reinforcement learning is essential for real-world deployment. Existing methods, however, often trade safety for performance by producing overly conservative policies or diffuse cost estimates that weaken policy gradients. We propose the Uncertain Safety Critic (USC), which modulates conservatism usingcriticuncertaintyandrefinesunder-coveredregions, reducing safety violations by ≈ 40% while maintaining competitive or higher rewards and cutting cost-gradient error by ≈ 83%.

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ECAI Conference 2025 Conference Paper

Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding

Daniel Bethell
Simos Gerasimou
Radu Calinescu
Calum Imrie

Safe exploration of reinforcement learning (RL) agents is a critical activity for empowering their deployment in many real-world scenarios. When prior knowledge of the target domain or task is unavailable, training RL agents in unknown, black-box environments unavoidably yields significant safety risks. Our ADVICE (Adaptive Shielding with a Contrastive Autoencoder) novel post-shielding approach operates in continuous state and action spaces, distinguishing safe and unsafe features of state-action pairs during training, and uses this knowledge to safeguard the RL agent from executing actions that yield likely hazardous outcomes. Our comprehensive experimental evaluation shows that ADVICE significantly reduces safety violations (≈50%) compared to state-of-the-art safe RL exploration approaches, while maintaining a competitive outcome reward for the synthesised safe policy.

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NeurIPS Conference 2025 Conference Paper

Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows

Adriel Sosa Marco
John D. Kirwan
Alexia Toumpa
Simos Gerasimou

Quantifying uncertainty in deep regression models is important both for understanding the confidence of the model and for safe decision-making in high-risk domains. Existing approaches that yield prediction intervals overlook distributional information, neglecting the effect of multimodal or asymmetric distributions on decision-making. Similarly, full or approximated Bayesian methods, while yielding the predictive posterior density, demand major modifications to the model architecture and retraining. We introduce MCNF, a novel post hoc uncertainty quantification method that produces both prediction intervals and the full conditioned predictive distribution. MCNF operates on top of the underlying trained predictive model; thus, no predictive model retraining is needed. We provide experimental evidence that the MCNF-based uncertainty estimate is well calibrated, is competitive with state-of-the-art uncertainty quantification methods, and provides richer information for downstream decision-making tasks

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AAAI Conference 2024 Conference Paper

Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction

Daniel Bethell
Simos Gerasimou
Radu Calinescu

Deploying deep learning models in safety-critical applications remains a very challenging task, mandating the provision of assurances for the dependable operation of these models. Uncertainty quantification (UQ) methods estimate the model’s confidence per prediction, informing decision-making by considering the effect of randomness and model misspecification. Despite the advances of state-of-the-art UQ methods, they are computationally expensive or produce conservative prediction sets/intervals. We introduce MC-CP, a novel hybrid UQ method that combines a new adaptive Monte Carlo (MC) dropout method with conformal prediction (CP). MC-CP adaptively modulates the traditional MC dropout at runtime to save memory and computation resources, enabling predictions to be consumed by CP, yielding robust prediction sets/intervals. Throughout comprehensive experiments, we show that MC-CP delivers significant improvements over comparable UQ methods, like MC dropout, RAPS and CQR, both in classification and regression benchmarks. MC-CP can be easily added to existing models, making its deployment simple. The MC-CP code and replication package is available at https://github.com/team-daniel/MC-CP.

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